ObjectiveTo clearly identify the difference between rescue injuries and agonal injuries and to avoid duplicate identifications and misidentifications. MethodsBased on the forensic pathological data of 5 923 cases of death cause identification from 2013 to 2022 in Sun Yat-sen University Forensic Identification Center and Guangzhou Tianhe District Branch of Guangzhou Public Security Bureau， this study retrospectively studied the characteristics of rescue injuries and agonal injuries seen in cause of death identification and their influence on cause of death identification. ResultsAmong all the 5 923 cases， 640 cases were found to have rescue injuries or agonal injuries， and 624 cases received treatment， of which 609 cases were found to have rescue injuries （97.60%）， 44 cases were found to have agonal injuries， and 13 cases were found to have both types of injuries. Among the 640 cases， 441 were male and 199 were female. The age of death was discontinuously distributed from 0 to 95 years old. The leading cause of death was disease， followed by mechanical injury and asphyxia. The main manifestations of rescue injuries were rib and sternum fractures， soft tissue injuries in the prechest area or face， and pericardial rupture. The most common injuries in agonal stage were falling after unconsciousness， inhalation of foreign body in respiratory tract or multiple violent injuries. Among the 640 cases， 19 cases were repeatedly identified， including 15 cases of rescue injuries， 6 cases of agonal injuries， and 2 cases of both types of injuries. Compared with the cases where neither type of injuries was detected， the repeated identification rate of treatment injuries and agonal injuries was significantly increased （χ²=4.04， P=0.044； χ²=43.49， P<0.001）. Among the 640 cases， 11 cases （1.72%） were misidentified as the initial injuries in the first identification， and 13 cases had combined rescue injuries or agonal injuries that were involved in death. ConclusionsBy elucidating the epidemiological characteristics of the two types of injuries， this study proved that the two types of injuries were associated with higher rates of repeated identification and misidentification， which provided a reference for reducing repeated identification and misidentification and improving the accuracy of cause of death identification.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

BACKGROUND: Delayed platelet engraftment is a common complication after umbilical cord blood transplantation (UCBT), and there is no standard therapy. Avatrombopag (AVA) is a second-generation thrombopoietin (TPO) receptor agonist (TPO-RA) that has shown efficacy in immune thrombocytopenia (ITP). However, few reports have focused on its efficacy in patients diagnosed with thrombocytopenia after allogeneic hematopoietic stem cell transplantation (allo-HSCT). METHODS: We conducted a retrospective study at the First Affiliated Hospital of the University of Science and Technology of China to evaluate the efficacy of AVA as a first-line TPO-RA in 65 patients after UCBT; these patients were compared with 118 historical controls. Response rates, platelet counts, megakaryocyte counts in bone marrow, bleeding events, adverse events and survival rates were evaluated in this study. Platelet reconstitution differences were compared between different medication groups. Multivariable analysis was used to explore the independent beneficial factors for platelet implantation. RESULTS: Fifty-two patients were given AVA within 30 days post-UCBT, and the treatment was continued for more than 7 days to promote platelet engraftment (AVA group); the other 13 patients were given AVA for secondary failure of platelet recovery (SFPR group). The median time to platelet engraftment was shorter in the AVA group than in the historical control group (32.5 days vs . 38.0 days, Z  = 2.095, P  = 0.036). Among the 52 patients in the AVA group, 46 achieved an overall response (OR) (88.5%), and the cumulative incidence of OR was 91.9%. Patients treated with AVA only had a greater 60-day cumulative incidence of platelet engraftment than patients treated with recombinant human thrombopoietin (rhTPO) only or rhTPO combined with AVA (95.2% vs . 84.5% vs . 80.6%, P  <0.001). Patients suffering from SFPR had a slightly better cumulative incidence of OR (100%, P  = 0.104). Patients who initiated AVA treatment within 14 days post-UCBT had a better 60-day cumulative incidence of platelet engraftment than did those who received AVA after 14 days post-UCBT (96.6% vs . 73.9%, P  = 0.003). CONCLUSION Compared with those in the historical control group, our results indicate that AVA could effectively promote platelet engraftment and recovery after UCBT, especially when used in the early period (≤14 days post-UCBT).
Humans ; Female ; Male ; Thrombocytopenia/etiology* ; Adult ; Retrospective Studies ; Cord Blood Stem Cell Transplantation/adverse effects* ; Middle Aged ; Adolescent ; Young Adult ; Thiazoles/adverse effects* ; Platelet Count ; Receptors, Thrombopoietin/agonists* ; Child ; Thiophenes

To investigate the impact of preoperative serum follicle-stimulating hormone (FSH) levels on the probability of testicular sperm retrieval, we conducted a study of nonobstructive azoospermic (NOA) men with different testicular volumes (TVs) who underwent microdissection testicular sperm extraction (micro-TESE). A total of 177 NOA patients undergoing micro-TESE for the first time from April 2019 to November 2022 in Shenzhen Zhongshan Obstetrics and Gynecology Hospital (formerly Shenzhen Zhongshan Urology Hospital, Shenzhen, China) were retrospectively reviewed. The subjects were divided into four groups based on average TV quartiles. Serum hormone levels in each TV group were compared between positive and negative sperm retrieval subgroups. Overall sperm retrieval rate was 57.6%. FSH levels (median [interquartile range]) were higher in the positive sperm retrieval subgroup compared with the negative outcome subgroup when average TV was <5 ml (first quartile [Q1: TV <3 ml]: 43.32 [17.92] IU l -1 vs 32.95 [18.56] IU l -1 , P = 0.048; second quartile [Q2: 3 ml ≤ TV <5 ml]: 31.31 [15.37] IU l -1 vs 25.59 [18.40] IU l -1 , P = 0.042). Elevated serum FSH levels were associated with successful micro-TESE sperm retrieval in NOA men whose average TVs were <5 ml (adjusted odds ratio [OR]: 1.06 per unit increase; 95% confidence interval [CI]: 1.01-1.11; P = 0.011). In men with TVs ≥5 ml, larger TVs were associated with lower odds of sperm retrieval (adjusted OR: 0.84 per 1 ml increase; 95% CI: 0.71-0.98; P = 0.029). In conclusion, elevated serum FSH levels were associated with positive sperm retrieval in micro-TESE in NOA men with TVs <5 ml. In men with TV ≥5 ml, increases in average TVs were associated with lower odds of sperm retrieval.
Humans ; Male ; Azoospermia/surgery* ; Sperm Retrieval/statistics & numerical data* ; Adult ; Follicle Stimulating Hormone/blood* ; Retrospective Studies ; Testis/pathology* ; Microdissection ; Organ Size

Investigating the correlation between micronucleus formation and male infertility has the potential to improve clinical diagnosis and deepen our understanding of pathological progression. Our study enrolled 2252 male patients whose semen was analyzed from March 2023 to July 2023. Their clinical data, including semen parameters and age, were also collected. Genetic analysis was used to determine whether the sex chromosome involved in male infertility was abnormal (including the increase, deletion, and translocation of the X and Y chromosomes), and subsequent semen analysis was conducted for clinical grouping purposes. The participants were categorized into five groups: normozoospermia, asthenozoospermia, oligozoospermia, oligoasthenozoospermia, and azoospermia. Patients were randomly selected for further study; 41 patients with normozoospermia were included in the control group and 117 patients with non-normozoospermia were included in the study group according to the proportions of all enrolled patients. Cytokinesis-block micronucleus (CBMN) screening was conducted through peripheral blood. Statistical analysis was used to determine the differences in micronuclei (MNi) among the groups and the relationships between MNi and clinical data. There was a significant increase in MNi in infertile men, including those with azoospermia, compared with normozoospermic patients, but there was no significant difference between the genetic and nongenetic groups in azoospermic men. The presence of MNi was associated with sperm concentration, progressive sperm motility, immotile spermatozoa, malformed spermatozoa, total sperm count, and total sperm motility. This study underscores the potential utility of MNi as a diagnostic tool and highlights the need for further research to elucidate the underlying mechanisms of male infertility.
Humans ; Male ; Infertility, Male/genetics* ; Adult ; Micronucleus Tests ; Semen Analysis ; Oligospermia/genetics* ; Azoospermia/genetics* ; Chromosome Aberrations ; Sperm Count ; Micronuclei, Chromosome-Defective ; Middle Aged

Recent studies have shown that shorter periods of ejaculatory abstinence may enhance certain sperm parameters, but the molecular mechanisms underlying these improvements are still unclear. This study explored whether reduced abstinence periods could improve semen quality, particularly for use in assisted reproductive technologies (ART). We analyzed semen samples from men with normal sperm counts ( n = 101) and those with low sperm motility or concentration ( n = 53) after 3-7 days of abstinence and then after 1-3 h of abstinence, obtained from the Reproductive & Genetic Hospital of CITIC-Xiangya (Changsha, China). Physiological and biochemical sperm parameters were evaluated, and the dynamics of transfer RNA (tRNA)-derived fragments (tRFs) were analyzed using deep RNA sequencing in five consecutive samples from men with normal sperm counts. Our results revealed significant improvement in sperm motility and a decrease in the DNA fragmentation index after the 1- to 3-h abstinence period. Additionally, we identified 245 differentially expressed tRFs, and the mitogen-activated protein kinase (MAPK) signaling pathway was the most enriched. Further investigations showed significant changes in tRF-Lys-TTT and its target gene mitogen-activated protein kinase kinase 2 ( MAP2K2 ), which indicates a role of tRFs in improving sperm function. These findings provide new insights into how shorter abstinence periods influence sperm quality and suggest that tRFs may serve as biomarkers for male fertility. This research highlights the potential for optimizing ART protocols and improving reproductive outcomes through molecular approaches that target sperm function.
Male ; Humans ; Spermatozoa/metabolism* ; RNA, Transfer/genetics* ; Sperm Motility/genetics* ; Adult ; Semen Analysis ; Sexual Abstinence/physiology* ; Sperm Count ; DNA Fragmentation